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Record W2005433218 · doi:10.1080/15325008.2013.763312

Fault Location of Uncompensated/Series-compensated Lines Using Two-end Synchronized Measurements

2013· article· en· W2005433218 on OpenAlexaboutno aff
Almoataz Y. Abdelaziz, S. F. Mekhamer, M. Ezzat

Bibliographic record

VenueElectric Power Components and Systems · 2013
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsnot available
Fundersnot available
KeywordsPhasorFault (geology)Phasor measurement unitElectric power transmissionMATLABSeries (stratigraphy)Transmission lineUnits of measurementLine (geometry)Computer scienceCompensation (psychology)EngineeringReal-time computingAlgorithmElectronic engineeringElectric power systemElectrical engineeringMathematicsPower (physics)

Abstract

fetched live from OpenAlex

Phasor measurement units have gained great popularity in the field of control and wide-area protection during the last decade. One of the major protection applications based on phasor measurement unit technology is to determine the fault location in a transmission line. Therefore, research has been developed in the field of fault location. This article introduces a generalized model of fault-location algorithm for both uncompensated and series-compensated transmission lines. The proposed algorithm is based on the distributed parameters of the transmission line in the determination of fault location. Moreover, the proposed algorithm utilizes the synchronized measurements of voltages and currents at both ends of the line. The proposed algorithm is general for any allocation of series-compensation elements. The proposed algorithm is tested through the PSCAD offline simulation program (Manitoba Research Center, Manitoba, Canada) and mathematical analysis with the aid of MATLAB (The MathWorks, Natick, Massachusetts, USA).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.234
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2013
Admission routes1
Has abstractyes

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